Time Series Link Prediction Using NMF

Mutinda, Faith W.; Nakashima, Atsuhiro; Takeuchi, Koh; Sasaki, Yu; Onizuka, Makoto

doi:10.2197/ipsjjip.27.752

Cited by 9 publications

(8 citation statements)

References 21 publications

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“…Then, they obtained the similarity value matrix of the network according to the result of matrix factorization, and link prediction was performed. Mutinda et al [16] applied non-negative matrix factorization to extract the latent features of the time-series graphs, and the time-series prediction method Holt-Winters was used to learn and extract the time-domain information of the above features to solve the link prediction problem.…”

Section: B Matrix Factorization-based Prediction Methodsmentioning

confidence: 99%

A Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network

2020

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Link prediction is to estimate the possibility of future links among nodes by utilizing known information such as network topology and node attributes. According to the characteristics of opportunistic networks (topological time-variation, node mobility and intermittent connections), this paper proposes a novel link prediction approach (IRWR-DBN) for opportunistic networks that is based on random walk and a deep belief network. First, we reconstruct the Markov probability transition matrix and define a similarity index-improved random walk with restart (IRWR). Second, we divide the opportunistic network into network snapshots. Then, the similarity matrix of each snapshot is calculated by using the IRWR index to construct a sample set. Finally, a predictive model is constructed based on a deep belief network which extracts the time-domain characteristics in the process of dynamic evolution of the opportunistic network. The experimental results on the ITC and MIT Reality datasets show that compared with methods, such as the similarity-based index (CN, AA, Katz, RA, RWR), convolutional neural network, and recurrent neural network, the proposed method is more accurate and stable. INDEX TERMS Opportunistic network, link prediction, random walk with restart, deep belief network, similarity index.

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Section: B Matrix Factorization-based Prediction Methodsmentioning

confidence: 99%

A Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network

2020

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“…The proposed method produced better results in terms of link prediction accuracy when compared with other methods. A recent study based on matrix factorization was discussed in [30]. Although the method of [30] achieved high prediction accuracy, the data was relatively limited as it was confined to sales datasets.…”

Section: Matrix Factorization Methodsmentioning

confidence: 99%

“…A recent study based on matrix factorization was discussed in [30]. Although the method of [30] achieved high prediction accuracy, the data was relatively limited as it was confined to sales datasets. As such, further evaluation involving more complex datasets is required to confirm its level of prediction accuracy.…”

Section: Matrix Factorization Methodsmentioning

confidence: 99%

“…A myriad of methods and algorithms has been introduced by many research field scholars. A current approach that aims to address the problem of low link prediction accuracy, which is based on matrix factorization, is proposed by [30]. The experimental results of the method indicate that it achieves a higher prediction accuracy than existing methods.…”

Section: Predicting Links In Snmentioning

confidence: 99%

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Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

Mohameds¹,

Zakis²,

Marjans³

2019

Adv. sci. technol. eng. syst. j.

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In more recent times, researchers have turned their attention to link prediction and the role link inference can play in better understanding the evolutionary nature of social networking sites. The objective of this paper is to present an in-depth review, analysis, and discussion of the cutting-edge link prediction methods that can be applied to better understand the development of social networks. The findings of the literature review reveal that there has been a steady increase in the number of published articles that present novel link prediction models that are designed to enhance the efficiency and accuracy of link prediction. In this paper, this most recent techniques to be proposed in this regard are compared and categorized, and features and evaluation metrics are presented for each approach. The results of the evaluation reveal that there are no complete or definitive methods available that can accurately and reliably be applied within different dynamic social networks to predict missing, emerging, and broken links within the network. The paper concludes by presenting potential future directions and recommendations for further studies

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“…Because NMF has no negative value, some scholars have applied it to the field of feature extraction. Mutinda et al, " [22] put forward the method of combining NMF and Holt-Winters to recommend the link service of sequences, the recommendation idea is to extract features by using NMF, then capture the changes of features with time by using Holt-Winters method, and finally recommend the link service of sequences to users by using unchanged features. Unlike this work, in this paper, we propose a hybrid recommendation based on combining content and the FCM clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

Duan¹,

Wang²,

Han³

2021

KSII TIIS

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A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the coldstart problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

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Time Series Link Prediction Using NMF

Cited by 9 publications

References 21 publications

A Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network

A Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

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